In a cellular communication system, a key task of a mobile terminal is to synchronize its internal reference clock to the carrier frequency of the serving or other nearby base station. Frequency synchronization is needed not only to enable proper reception of the radio signal transmitted from the base station, but also to ensure that the frequency of radio signals emitted from mobile terminal meets tight system specifications, so that substantial interference is not generated for other users.
To maintain a proper frequency reference for its oscillator, a mobile terminal typically performs periodic estimation of the frequency offset, i.e., the deviation of a local reference signal from the actual frequency of the transmitted signal, based on a signal received from a serving or monitored base station. The resulting frequency-offset estimates are used to adjust the reference frequency in the oscillator to keep it from drifting away from the correct designated frequency, to compensate digital signal processing performed on received signals, or both. An efficient algorithm for accurately estimating the frequency offset from the received signal is thus essential to the normal operation of a mobile terminal.
Many conventional frequency estimation algorithms require the receiver to have certain knowledge about the actual transmitted signal, which might be derived either through the use of a pre-determined training signal or through the use of demodulated information symbols in a decision-directed manner. However, in many cases the training signal may be too short for the receiver to derive an accurate frequency-offset estimate, while decision-directed estimation can be computationally expensive and may require the use of specialized hardware accelerator that lacks the flexibility for future design enhancement.
Moreover, many conventional decision-feedback frequency-estimation algorithms treat the baseband received signal as a complex-valued signal, thus treating in-phase and quadrature components of the received signal as the real and imaginary parts, respectively, of a complex-valued signal. These algorithms are generally derived assuming that complex arithmetic operations are used throughout the receiver. As a result, these algorithms are incompatible with receivers that treat the in-phase and quadrature components of the received signal as separate “spatial” dimensions, or branches, and apply more general two-dimensional “spatial” operations on the received signal. For example, a GSM single-antenna interference cancellation (SAIC) receiver typically treats the in-phase and quadrature components of the received signal as though they come from two different antenna elements. The spatial operations involved in such receivers are essential to the interference cancellation capability, but can also significantly distort the phase information of the corresponding complex-valued signal. Consequently, specially designed algorithms are needed for estimating frequency offset in such receivers.